High throughput serving of large language models (LLMs) requires batching sufficiently
many requests at a time. However, existing systems struggle because the key-value cache (KV …

Feedback controls based on the receding horizon method have proven to be a useful and
easy tool in stabilizing linear ordinary differential systems. In this paper the receding horizon …

Pruning is an effective way to reduce the huge inference cost of Transformer models. However,
prior work on pruning Transformers requires retraining the models. This can add high …

Row Hammer is a serious security threat to modern computing systems using DRAM as main
memory. It causes charge loss in DRAM cells adjacent to a frequently activated aggressor …

Deep learning (DL) frameworks take advantage of GPUs to improve the speed of DL inference
and training. Ideally, DL frameworks should be able to fully utilize the computation power …

To comply with the increasing number of government regulations about data placement and
processing, and to protect themselves against major cloud outages, many users want the …

Efficient deployment of transformer models in practice is challenging due to their inference
cost including memory footprint, latency, and power consumption, which scales quadratically …

In this paper, a novel data link layer protocol adopted in the ATSC 3.0 terrestrial broadcast
standard is described. The data link layer is a protocol layer between the physical layer and …

Background The cause of early septic failure after two-stage exchange revision total knee
arthroplasty (TKA) for chronic periprosthetic joint infection (PJI) and the factors affecting it are …

Reducing the inference latency of large language models (LLMs) is crucial, and speculative
decoding (SD) stands out as one of the most effective techniques. Rather than letting the …